{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bedrock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
    "> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
    "> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
    "> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
    "> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
    "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
    "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
    "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
    "> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.llms import Bedrock\n",
    "\n",
    "llm = Bedrock(\n",
    "    credentials_profile_name=\"bedrock-admin\", model_id=\"amazon.titan-text-express-v1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using in a conversation chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "conversation = ConversationChain(\n",
    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
    ")\n",
    "\n",
    "conversation.predict(input=\"Hi there!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversation Chain With Streaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.llms import Bedrock\n",
    "\n",
    "llm = Bedrock(\n",
    "    credentials_profile_name=\"bedrock-admin\",\n",
    "    model_id=\"amazon.titan-text-express-v1\",\n",
    "    streaming=True,\n",
    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversation = ConversationChain(\n",
    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
    ")\n",
    "\n",
    "conversation.predict(input=\"Hi there!\")"
   ]
  }
 ],
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